Bayesian inference

Modelling the dynamics of senescence spread

This study models how senescence spreads between cells and finds that differences in secreted signalling molecules and timedependent paracrine signalling limit its spread.

HaploCart: Human mtDNA Haplogroup Classification Using a Pangenomic Reference Graph

HaploCart classifies human mtDNA haplogroups using a pangenomic reference graph and Bayesian inference, outperforming existing tools with more accurate and unbiased confidence scores.

Bayesian Differential Analysis of Cell Type Proportions

This paper introduces a Bayesian multinomial regression method for analyzing cell type proportions without requiring a reference cell type, using the rjags package in R.

Single-cell Ca2+ parameter inference reveals how transcriptional states inform dynamic cell responses

The paper develops Bayesian methods for inferring dynamic parameters in single cells using gene expression and Ca2+ data, showing that transfer learning improves inference when cells are ordered by transcriptional similarity.

From Bayes to Darwin: evolutionary search as an exaptation from sampling-based Bayesian inference

The paper demonstrates how Darwinian evolution can emerge from Bayesian inference through collective selection in high-dimensional environments.

Fast and flexible Bayesian Jolly Seber models and application to populations with transients

This paper introduces a marginalized Bayesian Jolly Seber model that significantly improves computational efficiency for open population analysis, demonstrating speed gains of up to 1,000 times with minimal loss of accuracy.

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

This paper develops a Bayesian framework integrating MEG and 7T-MRS to infer synaptic connectivity influenced by neurotransmitter concentrations, confirming GABA and glutamate effects on specific cortical connections.

SpatialSort: A Bayesian Model for Clustering and CellPopulation Annotation of Spatial Proteomics Data

SpatialSort is a Bayesian clustering method for spatial proteomics data that integrates spatial information and prior knowledge to improve cell population annotation.

Sex differences in human musicality are negligible

This study finds no significant sex differences in music perception or production abilities across over 890,000 participants globally.

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data

This paper introduces filter inference, a scalable method for estimating NLME model parameters from snapshot time series data, enabling efficient analysis of large datasets.

Conflict between short- and long-term experiences affects visual perception by modulating sensory or motor response system: evidence from Bayesian inference models

The paper investigates how conflict between short- and long-term experiences affects visual perception using Bayesian inference models and finds that it modulates sensory or motor response systems.

Associations of schizophrenia with arrhythmic disorders and electrocardiogram traits: an in-depth genetic exploration of population samples

This study investigates genetic links between schizophrenia and arrhythmic disorders, finding specific genetic correlations and causal effects on Brugada syndrome and heart rate.

Variational inference accelerates accurate DNA mixture deconvolution

This paper shows that variational inference methods like SVGD and VI can speed up DNA mixture deconvolution with minimal loss of accuracy.

Bayesian Inference of Chemical Mixtures in Risk Assessment Incorporating the Hierarchical Principle

The paper develops a Bayesian method for analyzing chemical mixtures in risk assessment, incorporating prior knowledge about interactions and main effects.

Commonly used Bayesian diversification-rate models produce biologically meaningful differences on empirical phylogenies

This paper compares five Bayesian methods for inferring diversification rate shifts on empirical phylogenies and finds they produce different results due to model differences.

An Empirical Bayes Method for Differential Expression Analysis of Single Cells with Deep Generative Models

The paper introduces lvm-DE, a Bayesian method for differential expression analysis in scRNA-seq using deep generative models, improving log fold change estimation and differential gene detection.

Bayesian Approach Enabled Objective Comparison of Multiple Human iPSC-derived Cardiomyocytes' Proarrhythmia Sensitivities.

The paper uses Bayesian statistics to objectively compare proarrhythmic sensitivities of five human iPSC-derived cardiomyocyte lines to ten compounds, identifying differences in calcium transient responses.

Bayesian updating for self-assessment explains social dominance and winner-loser effects

The paper uses Bayesian updating in an agent-based model to explain winner-loser effects in animal contests and provides testable predictions for future studies.

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

This paper benchmarks Bayesian deep learning models for predicting cancer of unknown primary, showing improved uncertainty generalisation and accuracy under distributional shifts.

Matrix prior for data transfer between single cell data types in latent Dirichlet allocation

The paper introduces a matrix prior for latent Dirichlet allocation to improve cell type detection in small scATAC-seq datasets by leveraging information from existing data.

Statistical power and false positive rates for interdependent outcomes are strongly influenced by test type: Implications for behavioral neuroscience

The paper evaluates statistical methods for interdependent outcomes in behavioral neuroscience, finding high false positives with linear models and limited power with alternatives.

GARSA: An integrative pipeline for genome wide association studies and polygenic risk score inference in admixed human populations

GARSA is a pipeline for GWAS and PRS analysis in admixed populations, incorporating bias correction and multiple analytical methods.

A reliable and efficient adaptive Bayesian method to assess static lower limb position sense.

This paper introduces a reliable and efficient Bayesian method to assess static lower limb position sense using an adaptive algorithm and dual-belt treadmill.

Increasing information gain in animal research by improving statistical model accuracy

The paper improves statistical model accuracy in animal research to increase information gain per animal, showing benefits for small sample sizes.

Deconstructing the brain bases of emotion regulation: A systems-identification approach using Bayes factors

The paper identifies four neural systems related to cognitive reappraisal using Bayes factors and systems-identification in fMRI data from two studies.

Adaptive Bayesian Optimization for State-Dependent Brain Stimulation

This study develops and evaluates adaptive Bayesian optimization for state-dependent brain stimulation, showing it can find optimal parameters even when neural states are unknown or unmeasurable.

Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas

This paper introduces a Bayesian segmentation algorithm for thalamic nuclei using diffusion MRI and an improved histological atlas, showing improved accuracy in identifying nuclei and detecting Alzheimer's disease involvement.

Bayesian inference of spike-timing dependent plasticity learning rules from single neuron recordings in humans

The paper infers spike-timing dependent plasticity learning rules from human neural recordings using a Bayesian framework.

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

BayFlux quantifies metabolic fluxes and their uncertainty using Bayesian inference, showing genome-scale models reduce flux uncertainty compared to core models and improve knockout predictions.

Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs

The paper identifies high-resolution eQTLs in kidney tissues using Bayesian fine mapping with integrative priors, improving resolution and functional relevance.